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Journal articles on the topic 'ADMM schemes'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Manss, Christoph, and Dmitriy Shutin. "Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints." Applied Sciences 8, no. 10 (2018): 1722. http://dx.doi.org/10.3390/app8101722.

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This paper focuses on exploration when using different data distribution schemes and ADMM as a solver for swarms. By exploration, we mean the estimation of new measurement locations that are beneficial for the model estimation. In particular, the different distribution schemes are splitting-over-features or heterogeneous learning and splitting-over-examples or homogeneous learning. Each agent contributes a solution to solve the joint optimization problem by using ADMM and the consensus algorithm. This paper shows that some information is unknown to the individual agent, and thus, the estimatio

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Wang, Shuai, Yanqing Xu, Zhiguo Wang, Tsung-Hui Chang, Tony Q. S. Quek, and Defeng Sun. "Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10175–83. http://dx.doi.org/10.1609/aaai.v37i8.26212.

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As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery sti

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Zhang, Qin, Yutong Guan, Hai Li , and Zhengyu Song. "Distributed Cooperative Automatic Modulation Classification Using DWA-ADMM in Wireless Communication Networks." Electronics 12, no. 14 (2023): 3002. http://dx.doi.org/10.3390/electronics12143002.

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Automatic modulation classification (AMC) is an important component in non-cooperative wireless communication networks to identify the modulation schemes of the received signals. In this paper, considering the multipath effect in practical propagation environments, a distributed cooperative AMC (Co-AMC) network based on machine learning is proposed to identify the modulation scheme in non-cooperative wireless communication networks. Specifically, feature vectors are first obtained by applying a cyclic spectrum to facilitate the feature extraction of the received signal. Then, a classifier base

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Binfet, Philipp, Janis Adamek, Nils Schlüter, and Moritz Schulze Darup. "Towards privacy-preserving cooperative control via encrypted distributed optimization." at - Automatisierungstechnik 71, no. 9 (2023): 736–47. http://dx.doi.org/10.1515/auto-2023-0082.

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Abstract Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data are sensitive. Encrypted control has shown the potential to address this risk and ensure confidentiality. However, existing approaches mainly focus on cloud-based control and distributed schemes are restrictive. In this paper, we present a novel privacy-preserving cooperative control scheme based on encrypted distributed optimization. More precisely, we foc

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Ma, Shuli, Youchen Fan, Shengliang Fang, Weichao Yang, and Li Li. "Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme." Applied Sciences 15, no. 1 (2024): 322. https://doi.org/10.3390/app15010322.

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Employing low tensor rank decomposition in image inpainting has attracted increasing attention. This study exploited novel tensor arrangement schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of pixels. The developed arrangement schemes enhanced the low rankness of images under three tensor decomposition methods: matrix SVD, tensor train (TT) decomposition, and tensor singular value decomposition (t-SVD). By exploiting the schemes, we solved the image inpainting problem with three low-rank constrained models that use the matrix rank,

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Liu, Ruixia, Minglei Shu, and Changfang Chen. "ECG Signal Denoising and Reconstruction Based on Basis Pursuit." Applied Sciences 11, no. 4 (2021): 1591. http://dx.doi.org/10.3390/app11041591.

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The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original

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Costantini, Giuliano, and Daniel Görges. "Fast distributed model predictive control combining ADMM, IPM and Riccati iteration." at - Automatisierungstechnik 69, no. 2 (2021): 97–110. http://dx.doi.org/10.1515/auto-2020-0106.

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Abstract In model predictive control, the control action is found at each sampling time by solving an online optimization problem. Computationally, this step is very demanding, especially if compared to the evaluation of traditional control laws. This has limited the application of model predictive control to systems with slow dynamics for many years. Recently, several methods have been proposed in the literature which promise a substantial reduction of the computation time by either running the computation in parallel (distributed model predictive control) or exploiting the problem structure

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Xue, Feng, Xia Ai, and Jiaqi Liu. "On the convergence of recursive SURE for total variation minimization." Journal of Inverse and Ill-posed Problems 29, no. 2 (2021): 203–17. http://dx.doi.org/10.1515/jiip-2018-0012.

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Abstract Recently, total variation (TV) regularization has become a standard technique for image recovery. The mean squared error (MSE) of the reconstruction can be reliably estimated by Stein’s unbiased risk estimate (SURE). In this work, we develop two recursive evaluations of SURE, based on Chambolle’s projection method (CPM) for TV denoising and alternating direction method of multipliers (ADMM) for TV deconvolution, respectively. In particular, from the proximal point perspective, we provide the convergence analysis for both iterative schemes and the corresponding Jacobian recursions, in

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Calcan, Andrew, and Scott B. Lindstrom. "The ADMM algorithm for audio signal recovery and performance modification with the dual Douglas-Rachford dynamical system." AIMS Mathematics 9, no. 6 (2024): 14640–57. http://dx.doi.org/10.3934/math.2024712.

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<abstract><p>Practitioners employ operator splitting methods—such as alternating direction method of multipliers (ADMM) and its "dual" Douglas-Rachford method (DR)—to solve many kinds of optimization problems. We provide a gentle introduction to these algorithms, and illustrations of their duality-like relationship in the context of solving basis pursuit problems for audio signal recovery. Recently, researchers have used the dynamical systems associated with the iterates of splitting methods to motivate the development of schemes to improve performance. These developments include a

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Wang, Jinpeng, Wenyu Jiang, Kaizhi Huang, and Xiaoli Sun. "A Communication Anti-Jamming Scheme Assisted by RIS with Angular Response." Entropy 25, no. 12 (2023): 1638. http://dx.doi.org/10.3390/e25121638.

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By optimizing the reconfigurable intelligent surface (RIS) reflection coefficients, the channel capacity of legitimate users can be increased, thereby enhancing the anti-jamming performance of communication systems. However, existing studies on RIS-assisted anti-jamming assume that there is no coupling between the RIS reflection coefficients and the incident angle of electromagnetic (EM) waves, which is quite unreasonable. Therefore, we consider the effect of the incident angle of EM waves on the reflection coefficients of the RIS and propose a communication anti-jamming scheme assisted by an

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Xiao, Ming, and Mikael Skoglund. "Coding for Large-Scale Distributed Machine Learning." Entropy 24, no. 9 (2022): 1284. http://dx.doi.org/10.3390/e24091284.

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This article aims to give a comprehensive and rigorous review of the principles and recent development of coding for large-scale distributed machine learning (DML). With increasing data volumes and the pervasive deployment of sensors and computing machines, machine learning has become more distributed. Moreover, the involved computing nodes and data volumes for learning tasks have also increased significantly. For large-scale distributed learning systems, significant challenges have appeared in terms of delay, errors, efficiency, etc. To address the problems, various error-control or performan

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Wang, Yiyang, Risheng Liu, Long Ma, and Xiaoliang Song. "Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-Convex Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1650–57. http://dx.doi.org/10.1609/aaai.v33i01.33011650.

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We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical algorithms, for optimizing univariate sub-problems with nice properties. From the other side, it also augments probabilities to receive desired results, by embedding advanced techniques in optimizations of realistic tasks. Integrating both numerical algorithms and advanced techn

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Ding, Mingjun, Xiaodong Yang, Rui Hu, Zhitao Xiao, Jun Tong, and Jiangtao Xi. "On Matrix Completion-Based Channel Estimators for Massive MIMO Systems." Symmetry 11, no. 11 (2019): 1377. http://dx.doi.org/10.3390/sym11111377.

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Large-scale symmetric arrays such as uniform linear arrays (ULA) have been widely used in wireless communications for improving spectrum efficiency and reliability. Channel state information (CSI) is critical for optimizing massive multiple-input multiple-output(MIMO)-based wireless communication systems. The acquisition of CSI for massive MIMO faces challenges such as training shortage and high computational complexity. For millimeter wave MIMO systems, the low-rankness of the channel can be utilized to address the challenge of training shortage. In this paper, we compared several channel est

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Yang, Shaochuan, Kaizhi Huang, Hehao Niu, Yi Wang, and Zheng Chu. "Weighted Sum Secrecy Rate Maximization for Joint ITS- and IRS-Empowered System." Entropy 25, no. 7 (2023): 1102. http://dx.doi.org/10.3390/e25071102.

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In this work, we investigate a novel intelligent surface-assisted multiuser multiple-input single-output multiple-eavesdropper (MU-MISOME) secure communication network where an intelligent reflecting surface (IRS) is deployed to enhance the secrecy performance and an intelligent transmission surface (ITS)-based transmitter is utilized to perform energy-efficient beamforming. A weighted sum secrecy rate (WSSR) maximization problem is developed by jointly optimizing transmit power allocation, ITS beamforming, and IRS phase shift. To solve this problem, we transform the objective function into an

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Gregoratti, David, Xavier Mestre, and Xavier Vilajosana. "Model-Aware Collision Resolution for High-Order Orthogonal Modulations." IEEE Wireless Communications Letters 9, no. 7 (2020): 957–61. https://doi.org/10.1109/LWC.2020.2975794.

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A novel method is proposed to resolve collisions in slotted-ALOHA random access schemes that rely on orthogonal modulations at the physical layer, a common choice in wireless sensor networks. The algorithm includes solutions from machine learning, regularized optimization and transportation theory to exploit the symmetry structure that characterizes the signal model at a reasonable computational cost. Simulation results corroborate the effectiveness of the new method as compared to previous solutions.

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Wei, Guofeng, Bangning Zhang, Guoru Ding, Bing Zhao, Yimin Wei, and Daoxing Guo. "Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks." Sensors 20, no. 7 (2020): 2005. http://dx.doi.org/10.3390/s20072005.

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For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and th

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Li, Guibing, Weidong Jin, Jiaqing Miao, et al. "Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function." Entropy 25, no. 3 (2023): 523. http://dx.doi.org/10.3390/e25030523.

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Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8–9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace fu

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Liu, Ning, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, and Jieping Ye. "AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4876–83. http://dx.doi.org/10.1609/aaai.v34i04.5924.

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Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperparameter determination process is necessary due to the large number of flexible hyperparameters. This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose a

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Agrawal, Rohit, Kapil Ahuja, Marc C. Steinbach, and Thomas Wick. "SABMIS: sparse approximation based blind multi-image steganography scheme." PeerJ Computer Science 8 (November 28, 2022): e1080. http://dx.doi.org/10.7717/peerj-cs.1080.

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We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using the Alternating Direction

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Zhang, Jiasen, Xiaomei Wang, Bin Wang, Weikai Sun, Haiyang Du, and Yuanke Zhao. "Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework." Sensors 23, no. 12 (2023): 5759. http://dx.doi.org/10.3390/s23125759.

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The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient

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Zhao, Taiyin, Luoxiao Ouyang, and Tian Chen. "Seismic Random Noise Attenuation via Low-Rank Tensor Network." Applied Sciences 15, no. 7 (2025): 3453. https://doi.org/10.3390/app15073453.

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Seismic data are easily contaminated by random noise, impairing subsequent geological interpretation tasks. Existing denoising methods like low-rank approximation (LRA) and deep learning (DL) show promising denoising capabilities but still have limitations; for instance, LRA performance is parameter-sensitive, and DL networks lack interpretation. As an alternative, this paper introduces the low-rank tensor network (LRTNet), an innovative approach that integrates low-rank tensor approximation (LRTA) with DL. Our method involves constructing a noise attenuation model that leverages LRTA, total v

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Huang, Zhiyong, Ziyan Luo, and Naihua Xiu. "High-Dimensional Least-Squares with Perfect Positive Correlation." Asia-Pacific Journal of Operational Research 36, no. 04 (2019): 1950016. http://dx.doi.org/10.1142/s0217595919500167.

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The least-squares is a common and important method in linear regression. However, it often leads to overfitting phenomenon as dealing with high-dimensional problems, and various regularization schemes regarding prior information for specific problems are studied to make up such a deficiency. In the sense of Kendall’s [Formula: see text] from the community of nonparametric analysis, we establish a new model wherein the ordinary least-squares is equipped with perfect positive correlation constraint, sought to maintain the concordance of the rankings of the observations and the systematic compone

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Kori, Jyoti, Alka Mahajan, and Manish Mandloi. "Performance evaluation of symbol detection algorithms in massive MIMO communication systems." Journal of Electrical Engineering 75, no. 5 (2024): 363–71. http://dx.doi.org/10.2478/jee-2024-0044.

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Abstract Massive MIMO (mMIMO) is the essential technique for attaining the exponential increase in the data rate needed for future communication systems. These advancements have been significantly supported by progress in VLSI technology, which enables the integration of an enormous number of antennas and the complex signal processing essential for massive MIMO systems on a single chip. This work conducts a comprehensive analysis of the intricacy of seven matrix decomposition techniques for symbol detection in future mMIMO communication systems: ADMM-based infinity norm (ADMIN), Neumann series

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Hao, Jianxin, and Qiang Wang. "A Class of Finite Difference Schemes with Adaptive Controllable Dispersion and Low Dissipation for Compressible Turbulence." Fluids 10, no. 2 (2025): 43. https://doi.org/10.3390/fluids10020043.

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The dispersion and dissipation properties of a numerical scheme are critical in simulating flow fields involving a wide range of length scales. In this study, we highlight the common oversight of focusing merely on controlling dispersion error without considering the importance of appropriate dispersion and scalability in computational efficiency. This study demonstrates that adjusting dispersion to match the local flow field near discontinuities is more effective in suppressing oscillations than simply minimizing dispersion. This proposed high-order finite difference scheme with adaptive disp

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Timotheou, Stelios, Christos Panayiotou, and Marios Polycarpou. "Distributed Traffic Signal Control Using the Cell Transmission Model via the Alternating Direction Method of Multipliers." IEEE Transactions on Intelligent Transportation Systems 16, no. 2 (2015): 919–33. https://doi.org/10.1109/TITS.2014.2347300.

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Traffic signal control is a key ingredient in intelligent transportation systems to increase the capacity of existing urban transportation infrastructure. However, to achieve optimal system-wide operation, it is essential to coordinate traffic signals at various intersections. In this paper, we model the multiple-intersection traffic signal control problem using the cell transmission model as a mixed-integer linear program. The solution of the problem is facilitated by its special structure, which allows both temporal and spatial decomposition. Temporal decomposition is employed to reduce the

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Shakya, Subarna. "Smart Fault Diagnostics using Convolutional Neural Network and Adam Stochastic Optimization." March 2021 3, no. 1 (2021): 38–46. http://dx.doi.org/10.36548/jscp.2021.1.005.

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Navigation, aviation and several other fields of engineering extensively make use of rotating machinery. The stability and safety of the equipment as well as the personnel are affected by this machinery. Use of deep learning as the basis of intelligent fault diagnosis schemes has and investigation of other relevant fault diagnosis schemes has a large scope for development. Thorough exploration needs to be performed in deep neural network (DNN) based schemes as shallow layer network structure based fault diagnosis schemes that are currently available has several considerable limitations. The no

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Hu, Jingyi, Junfeng Guo, Zhiyuan Rui, and Zhiming Wang. "ADMM-1DNet: Online Monitoring Method for Outdoor Mechanical Equipment Part Signals Based on Deep Learning and Compressed Sensing." Applied Sciences 14, no. 6 (2024): 2653. http://dx.doi.org/10.3390/app14062653.

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To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a detailed online vibration signal monitoring scheme. The basic approach of the ADMM-1DNet network is to map the update steps of the classical Alternating Direction Method of Multipliers (ADMM) into the deep network architecture with a fixed number of layers, and each phase corresponds to an iteration in the traditional ADMM. At the same time, what d

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Xie, Changxu, Huiqin Du, and Xialing Liu. "Deep Learning-Based Symbol-Level Precoding for Large-Scale Antenna System." Wireless Communications and Mobile Computing 2021 (March 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/6698424.

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In this work, we consider a multiple input multiple-output system with large-scale antenna array which creates unintended multiuser interference and increases the power consumption due to the large number of radio frequency (RF) chains. The antenna selective symbol level precoding design is developed by minimizing the symbol error rate (SER) with limits of available RF chains. The ℓ 0 -norm constrained nonconvex problem can be approximated as ℓ 1 -minimization, which is further solved by alternating direction method of multipliers (ADMM) approach. The basic ADMM scheme is mapped into iterative

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Zgoba, Kristen M., Michael Miner, Jill Levenson, Raymond Knight, Elizabeth Letourneau, and David Thornton. "The Adam Walsh Act." Sexual Abuse 28, no. 8 (2016): 722–40. http://dx.doi.org/10.1177/1079063215569543.

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This study was designed to compare the Adam Walsh Act (AWA) classification tiers with actuarial risk assessment instruments and existing state classification schemes in their respective abilities to identify sex offenders at high risk to re-offend. Data from 1,789 adult sex offenders released from prison in four states were collected (Minnesota, New Jersey, Florida, and South Carolina). On average, the sexual recidivism rate was approximately 5% at 5 years and 10% at 10 years. AWA Tier 2 offenders had higher Static-99R scores and higher recidivism rates than Tier 3 offenders, and in Florida, t

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Belyaev, Alexander, and Pierre-Alain Fayolle. "An ADMM-based scheme for distance function approximation." Numerical Algorithms 84, no. 3 (2019): 983–96. http://dx.doi.org/10.1007/s11075-019-00789-5.

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Abstract A novel variational problem for approximating the distance function (to a domain boundary) is proposed. It is shown that this problem can be efficiently solved by ADMM. A review of several other variational and PDE-based methods for distance function estimation is presented. Advantages of the proposed distance function estimation method are demonstrated by numerical experiments. Applications of the method to the problems of surface curvature estimation and computing the skeleton of a binary image are shown.

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Kochanek, Piotr. "Wpływ starożytnej schematyzacji mnemotechnicznej na kartografię średniowieczną i wczesnonowożytną." Vox Patrum 69 (December 16, 2018): 285–375. http://dx.doi.org/10.31743/vp.3264.

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The article analyzes the ancient geographical schemes seen on the 75 medieval and early modern maps. Here distinguishes three types of schemes: 1. geographical and geometric, 2. chorographic, 3. topographic. The first type is based on the Globe of Crates of Malos and the acrostic of the name Adam. The second type includes Sicily, having a triangle scheme; Sardinia, having the shape of a human foot; Cyclades and Orkney, taking the form of a circle; Italia, having the shape of a triangle, a quadrangle, bull horns or the famous „Stivale”; Spain also schematized in the form of a triangle; Alps p

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Xu, Changliang, Zhong Yang, Hao Xu, et al. "Model Predictive Control for an Aerial Tree-Pruning Robot Based on Alternating Direction Method of Multipliers." Complexity 2021 (June 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/9981123.

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Obstacles of some trees within the electric power transmission line channel are of great threat to the electricity supply. Nowadays, the tasks of clearing threatening tree branches are still mostly operated by hand and simple tools. In this article, an aerial tree-pruning robot with a novel structure is designed to improve the pruning operation efficiency and enhance the safety of the staff. However, the long arm of the pruning tool results in much higher rotational inertia of the robot, which brings difficulties for the robot to remain stable. Therefore, a control scheme based on model predic

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Fei, Zhiting, Jiachen Zhao, Zhe Geng, Xiaohua Zhu, and Jindong Zhang. "Hybrid FSK-PSK Waveform Optimization for Radar Based on Alternating Direction Method of Multiplier (ADMM)." Sensors 21, no. 23 (2021): 7915. http://dx.doi.org/10.3390/s21237915.

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In this paper, a new radar signal modulated with a hybrid of the frequency shift keying (FSK) and the phase shift keying (PSK) signal—i.e., the FSK-PSK signal—is studied. Different phase encoding sequences are used to modulate the sub-pulses to obtain lower sidelobe levels and ensure signal orthogonality. In addition, to counter intra-pulse slice repeater jamming of specific length generated by the enemy jammer, an orthogonal waveform made of sub-pulses of equal length based on the FSK-PSK modulation scheme is designed. The simulation results show that the optimized discrete phase encoding seq

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Liu, Sijia, Parikshit Ram, Deepak Vijaykeerthy, et al. "An ADMM Based Framework for AutoML Pipeline Configuration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4892–99. http://dx.doi.org/10.1609/aaai.v34i04.5926.

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We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed va

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Chen, Liang, Xiaokai Chang, and Sanyang Liu. "A Three-Operator Splitting Perspective of a Three-Block ADMM for Convex Quadratic Semidefinite Programming and Beyond." Asia-Pacific Journal of Operational Research 37, no. 04 (2020): 2040009. http://dx.doi.org/10.1142/s0217595920400096.

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In recent years, several convergent variants of the multi-block alternating direction method of multipliers (ADMM) have been proposed for solving the convex quadratic semidefinite programming via its dual, which is inherently a [Formula: see text]-block separable convex optimization problem with coupled linear constraints. Among these multi-block ADMM-type algorithms, the modified [Formula: see text]-block ADMM in [Chang, XK, SY Liu and X Li (2016). Modified alternating direction method of multipliers for convex quadratic semidefinite programming. Neurocomputing, 214, 575–586] bears a peculiar

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Zhang, Shougui, Xiyong Cui, Guihua Xiong, and Ruisheng Ran. "An Optimal ADMM for Unilateral Obstacle Problems." Mathematics 12, no. 12 (2024): 1901. http://dx.doi.org/10.3390/math12121901.

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We propose a new alternating direction method of multipliers (ADMM) with an optimal parameter for the unilateral obstacle problem. We first use the five-point difference scheme to discretize the problem. Then, we present an augmented Lagrangian by introducing an auxiliary unknown, and an ADMM is applied to the corresponding saddle-point problem. Through eliminating the primal and auxiliary unknowns, a pure dual algorithm is then used. The convergence of the proposed method is analyzed, and a simple strategy is presented for selecting the optimal parameter, with the largest and smallest eigenva

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Park, Kyeng Min, Choongik Kim, Samuel W. Thomas, et al. "New Encoding Schemes with Infofuses." Advanced Materials 23, no. 42 (2011): 4851–56. http://dx.doi.org/10.1002/adma.201103045.

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Shen, Feifan, Qiuwei Wu, Yan Xu, Fangxing Li, Fei Teng, and Goran Strbac. "Hierarchical service restoration scheme for active distribution networks based on ADMM." International Journal of Electrical Power & Energy Systems 118 (June 2020): 105809. http://dx.doi.org/10.1016/j.ijepes.2019.105809.

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Zhu, Cui Tao, Xu Jie Liu, Han Xin Wang, and Sheng Bin Liao. "A Fast Reweighted Alternating Direction Method for Wideband Spectrum Sensing." Applied Mechanics and Materials 513-517 (February 2014): 3777–80. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3777.

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In this paper, we propose a fast algorithm based on Reweighted Alternating Direction Method of Multiplier (R-ADMM) for cooperative compressive wideband spectrum sensing. The R-ADMM alternately updates the recovered signal matrix, the Lagrangian multiplier and the residue, and all update rules only involve matrix or vector multiplications and summations. To seek joint sparse solutions in a fully distributed scheme, multiple cognitive users collaborate during the sensing period by enforcing consensus among local spectral estimates. Meanwhile, adding weight in the target term and suppressing non-

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Yao, Yu, Xiaoning Zhu, Hongyu Dong, et al. "ADMM-based problem decomposition scheme for vehicle routing problem with time windows." Transportation Research Part B: Methodological 129 (November 2019): 156–74. http://dx.doi.org/10.1016/j.trb.2019.09.009.

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Smith, Stephen, Jenny Stocker, Martin Seaton, and David Carruthers. "Model inter-comparison and validation of ADMS plume chemistry schemes." International Journal of Environment and Pollution 62, no. 2/3/4 (2017): 395. http://dx.doi.org/10.1504/ijep.2017.089427.

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Carruthers, David, Martin Seaton, Stephen Smith, and Jenny Stocker. "Model inter-comparison and validation of ADMS plume chemistry schemes." International Journal of Environment and Pollution 62, no. 2/3/4 (2017): 395. http://dx.doi.org/10.1504/ijep.2017.10010443.

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Njima, Wafa, Rafik Zayani, Iness Ahriz, Michel Terre, and Ridha Bouallegue. "Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization." Applied Sciences 9, no. 12 (2019): 2414. http://dx.doi.org/10.3390/app9122414.

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In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented a

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Wang, Si, Ting-Zhu Huang, Xi-le Zhao, and Jun Liu. "An Alternating Direction Method for Mixed Gaussian Plus Impulse Noise Removal." Abstract and Applied Analysis 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/850360.

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A combined total variation and high-order total variation model is proposed to restore blurred images corrupted by impulse noise or mixed Gaussian plus impulse noise. We attack the proposed scheme with an alternating direction method of multipliers (ADMM). Numerical experiments demonstrate the efficiency of the proposed method and the performance of the proposed method is competitive with the existing state-of-the-art methods.

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Bernstein, Adam. "You're hired: apprenticeships and the new scheme." Nursing and Residential Care 23, no. 8 (2021): 1–4. http://dx.doi.org/10.12968/nrec.2021.23.8.5.

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Lord Sugar and Donald Trump popularised the term ‘apprentice’ and the phrase ‘you're fired!’, but there is much more to the concept. Implemented well, with willing candidates, apprenticeships are a great entrée into the worlds of learning and work. Adam Bernstein explains.

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Frankfurter, David. "Apocalypses Real and Alleged in the Mani Codex." Numen 44, no. 1 (1997): 60–73. http://dx.doi.org/10.1163/1568527972629876.

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AbstractThe florilegium of revelations that Mani adduces as proof of his own authority in the Cologne Mani Codex has stimulated research into the circulation and influence of Jewish apocalypses among the various Jewish-Christian sects of late antiquity. But it has also proved frustrating, since not one of the apocalyptic “texts” that Mani quotes matches extant apocalypses in the name of Enoch, Adam, Seth, or Enosh. Considering the breadth of the Enoch literature now known from textual and patristic sources, including Manichaean literature, the absence of a parallel for Mani's Enoch-“quotation”

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Zhou, Bo, Yu-Fei Yang, and Wei-Si Xie. "A Novel Model and ADMM Algorithm for MR Image Reconstruction." Mathematical Problems in Engineering 2018 (September 5, 2018): 1–9. http://dx.doi.org/10.1155/2018/5490458.

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Motivated by the ideas from the LOT model and its deformations, we propose a coupling model for the MR image reconstruction and apply the split Bregman iterative method on the proposed model by utilizing the augmented Lagrangian technique. The related minimization problem is then divided into four subproblems by means of the alternating minimization method. And on this basis, by combining the Barzilai-Borwein step size selection scheme, generalized shrinkage formulas, and the shrink operator, we propose an ADMM type algorithm to solve the proposed model. Several numerical examples are implemen

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Zhang, Liuyang, Jianjun Zhang, Huogen Yu, and Jiaguo Yu. "Emerging S‐Scheme Photocatalyst." Advanced Materials 34, no. 11 (2022): 2107668. http://dx.doi.org/10.1002/adma.202107668.

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Kumar Singh, Koushlendra, Suraj Kumar, Marios Antonakakis, et al. "Deep Learning Capabilities for the Categorization of Microcalcification." International Journal of Environmental Research and Public Health 19, no. 4 (2022): 2159. http://dx.doi.org/10.3390/ijerph19042159.

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Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed

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Chen, Yaling, Luxi Hao, and Gaowen Yin. "Distributed Energy Management of the Hybrid AC/DC Microgrid with High Penetration of Distributed Energy Resources Based on ADMM." Complexity 2021 (September 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/1863855.

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This paper aims to investigate energy management of the hybrid AC/DC microgrid with the high penetration of distributed energy resources (DERs), such as electrical vehicles, heat pumps, and photovoltaics. In the previous studies, energy management of the hybrid microgrid is usually carried out by the system operator in a centralized manner, which suffers from the compromise of privacy information protection and the risk of single-point failure. Therefore, this paper proposes a distributed energy management scheme of the hybrid microgrid using the projection function-based alternating direction

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